



'''simple docstring'''

import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import DecisionTransformerModel
			from transformers.models.decision_transformer.modeling_decision_transformer import (
			    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
			)

class 							a__:






			def __init__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase=13					,   _UpperCAmelCase=7					,   _UpperCAmelCase=6					,   _UpperCAmelCase=17					,   _UpperCAmelCase=23					,   _UpperCAmelCase=11					,   _UpperCAmelCase=True					,   )							->							int:
					snake_case__	     =parent
					snake_case__	     =batch_size
					snake_case__	     =seq_length
					snake_case__	     =act_dim
					snake_case__	     =state_dim
					snake_case__	     =hidden_size
					snake_case__	     =max_length
					snake_case__	     =is_training






			def 						_lowercase  (     self						)							->							Dict:
					snake_case__	     =floats_tensor((self.batch_size, self.seq_length, self.state_dim)						)
					snake_case__	     =floats_tensor((self.batch_size, self.seq_length, self.act_dim)						)
					snake_case__	     =floats_tensor((self.batch_size, self.seq_length, 1)						)
					snake_case__	     =floats_tensor((self.batch_size, self.seq_length, 1)						)
					snake_case__	     =ids_tensor((self.batch_size, self.seq_length)					,   vocab_size=1000						)
					snake_case__	     =random_attention_mask((self.batch_size, self.seq_length)						)

					snake_case__	     =self.get_config()

					return (
					    config,
					    states,
					    actions,
					    rewards,
					    returns_to_go,
					    timesteps,
					    attention_mask,
					)






			def 						_lowercase  (     self						)							->							List[str]:
					return DecisionTransformerConfig(
					    batch_size=self.batch_size					,   seq_length=self.seq_length					,   act_dim=self.act_dim					,   state_dim=self.state_dim					,   hidden_size=self.hidden_size					,   max_length=self.max_length					,   )






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   )							->							Union[str, Any]:
					snake_case__	     =DecisionTransformerModel(config=_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()
					snake_case__	     =model(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)

					self.parent.assertEqual(result.state_preds.shape					,   states.shape						)
					self.parent.assertEqual(result.action_preds.shape					,   actions.shape						)
					self.parent.assertEqual(result.return_preds.shape					,   returns_to_go.shape						)
					self.parent.assertEqual(
					    result.last_hidden_state.shape					,   (self.batch_size, self.seq_length * 3, self.hidden_size)						)  # seq length *3 as there are 3 modelities: states, returns and actions






			def 						_lowercase  (     self						)							->							int:
					snake_case__	     =self.prepare_config_and_inputs()
					(
					    (
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	
					)	     =config_and_inputs
					snake_case__	     ={
					    'states': states,
					    'actions': actions,
					    'rewards': rewards,
					    'returns_to_go': returns_to_go,
					    'timesteps': timesteps,
					    'attention_mask': attention_mask,
					}
					return config, inputs_dict





@require_torch
class 							a__( snake_case__   ,      snake_case__   ,      snake_case__   ,      unittest.TestCase     ):
			a_	: List[str]									=      (DecisionTransformerModel,) if is_torch_available() else ()
			a_	: Union[str, Any]									=      ()
			a_	: int									=      {'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

			# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
			a_	: Optional[int]									=      False

			# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
			a_	: int									=      False
			a_	: List[Any]									=      False
			a_	: Tuple									=      False
			a_	: Union[str, Any]									=      False
			a_	: Optional[Any]									=      False
			a_	: Optional[int]									=      False
			a_	: Tuple									=      False
			a_	: Dict									=      False
			a_	: Optional[int]									=      False






			def 						_lowercase  (     self						)							->							Dict:
					snake_case__	     =DecisionTransformerModelTester(self						)
					snake_case__	     =ConfigTester(self					,   config_class=_UpperCAmelCase					,   hidden_size=37						)






			def 						_lowercase  (     self						)							->							str:
					self.config_tester.run_common_tests()






			def 						_lowercase  (     self						)							->							Dict:
					snake_case__	     =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_UpperCAmelCase						)






			@slow
			def 						_lowercase  (     self						)							->							Optional[int]:
					for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							snake_case__	     =DecisionTransformerModel.from_pretrained(_UpperCAmelCase						)
							self.assertIsNotNone(_UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							str:
					snake_case__     ,	snake_case__	     =self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							snake_case__	     =model_class(_UpperCAmelCase						)
							snake_case__	     =inspect.signature(model.forward						)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							snake_case__	     =[*signature.parameters.keys()]

							snake_case__	     =[
							    'states',
							    'actions',
							    'rewards',
							    'returns_to_go',
							    'timesteps',
							    'attention_mask',
							]

							self.assertListEqual(arg_names[: len(_UpperCAmelCase						)]					,   _UpperCAmelCase						)





@require_torch
class 							a__( unittest.TestCase     ):






			@slow
			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =2  # number of steps of autoregressive prediction we will perform
					snake_case__	     =10  # defined by the RL environment, may be normalized
					snake_case__	     =DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-expert'						)
					snake_case__	     =model.to(_UpperCAmelCase						)
					snake_case__	     =model.config
					torch.manual_seed(0						)
					snake_case__	     =torch.randn(1					,   1					,   config.state_dim						).to(device=_UpperCAmelCase					,   dtype=torch.floataa						)  # env.reset()

					snake_case__	     =torch.tensor(
					    [[0.242_793, -0.28_693_074, 0.8_742_613], [0.67_815_274, -0.08_101_085, -0.12_952_147]]					,   device=_UpperCAmelCase						)

					snake_case__	     =torch.tensor(_UpperCAmelCase					,   device=_UpperCAmelCase					,   dtype=torch.floataa						).reshape(1					,   1					,   1						)
					snake_case__	     =state
					snake_case__	     =torch.zeros(1					,   0					,   config.act_dim					,   device=_UpperCAmelCase					,   dtype=torch.floataa						)
					snake_case__	     =torch.zeros(1					,   0					,   device=_UpperCAmelCase					,   dtype=torch.floataa						)
					snake_case__	     =torch.tensor(0					,   device=_UpperCAmelCase					,   dtype=torch.long						).reshape(1					,   1						)

					for step in range(_UpperCAmelCase						):
							snake_case__	     =torch.cat([actions, torch.zeros(1					,   1					,   config.act_dim					,   device=_UpperCAmelCase						)]					,   dim=1						)
							snake_case__	     =torch.cat([rewards, torch.zeros(1					,   1					,   device=_UpperCAmelCase						)]					,   dim=1						)

							snake_case__	     =torch.ones(1					,   states.shape[1]						).to(dtype=torch.long					,   device=states.device						)

							with torch.no_grad():
									snake_case__     ,	snake_case__     ,	snake_case__	     =model(
									    states=_UpperCAmelCase					,   actions=_UpperCAmelCase					,   rewards=_UpperCAmelCase					,   returns_to_go=_UpperCAmelCase					,   timesteps=_UpperCAmelCase					,   attention_mask=_UpperCAmelCase					,   return_dict=_UpperCAmelCase					,   )

							self.assertEqual(action_pred.shape					,   actions.shape						)
							self.assertTrue(torch.allclose(action_pred[0, -1]					,   expected_outputs[step]					,   atol=1E-4						)						)
							snake_case__     ,	snake_case__     ,	snake_case__     ,	snake_case__	     =(  # env.step(action)
							    torch.randn(1					,   1					,   config.state_dim						).to(device=_UpperCAmelCase					,   dtype=torch.floataa						),
							    1.0,
							    False,
							    {},
							)

							snake_case__	     =action_pred[0, -1]
							snake_case__	     =torch.cat([states, state]					,   dim=1						)
							snake_case__	     =returns_to_go[0, -1] - reward
							snake_case__	     =torch.cat([returns_to_go, pred_return.reshape(1					,   1					,   1						)]					,   dim=1						)
							snake_case__	     =torch.cat(
							    [timesteps, torch.ones((1, 1)					,   device=_UpperCAmelCase					,   dtype=torch.long						) * (step + 1)]					,   dim=1						)









'''simple docstring'''

import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


SCREAMING_SNAKE_CASE__     :						List[Any]	=  logging.get_logger(__name__)

enable_full_determinism()

class 							a__( snake_case__   ,      snake_case__   ,      unittest.TestCase     ):
			a_	: Dict									=      UNetaDModel
			a_	: List[Any]									=      '''sample'''






			@property
			def 						_lowercase  (     self						)							->							Tuple:
					snake_case__	     =4
					snake_case__	     =3
					snake_case__	     =(32, 32)

					snake_case__	     =floats_tensor((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor([10]						).to(_UpperCAmelCase						)

					return {"sample": noise, "timestep": time_step}






			@property
			def 						_lowercase  (     self						)							->							Optional[int]:
					return (3, 32, 32)






			@property
			def 						_lowercase  (     self						)							->							Optional[int]:
					return (3, 32, 32)






			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     ={
					    'block_out_channels': (32, 64),
					    'down_block_types': ('DownBlock2D', 'AttnDownBlock2D'),
					    'up_block_types': ('AttnUpBlock2D', 'UpBlock2D'),
					    'attention_head_dim': 3,
					    'out_channels': 3,
					    'in_channels': 3,
					    'layers_per_block': 2,
					    'sample_size': 32,
					}
					snake_case__	     =self.dummy_input
					return init_dict, inputs_dict





class 							a__( snake_case__   ,      snake_case__   ,      unittest.TestCase     ):
			a_	: Union[str, Any]									=      UNetaDModel
			a_	: Optional[Any]									=      '''sample'''






			@property
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     =4
					snake_case__	     =4
					snake_case__	     =(32, 32)

					snake_case__	     =floats_tensor((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor([10]						).to(_UpperCAmelCase						)

					return {"sample": noise, "timestep": time_step}






			@property
			def 						_lowercase  (     self						)							->							Optional[int]:
					return (4, 32, 32)






			@property
			def 						_lowercase  (     self						)							->							Dict:
					return (4, 32, 32)






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     ={
					    'sample_size': 32,
					    'in_channels': 4,
					    'out_channels': 4,
					    'layers_per_block': 2,
					    'block_out_channels': (32, 64),
					    'attention_head_dim': 32,
					    'down_block_types': ('DownBlock2D', 'DownBlock2D'),
					    'up_block_types': ('UpBlock2D', 'UpBlock2D'),
					}
					snake_case__	     =self.dummy_input
					return init_dict, inputs_dict






			def 						_lowercase  (     self						)							->							Dict:
					snake_case__     ,	snake_case__	     =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'					,   output_loading_info=_UpperCAmelCase						)

					self.assertIsNotNone(_UpperCAmelCase						)
					self.assertEqual(len(loading_info['missing_keys']						)					,   0						)

					model.to(_UpperCAmelCase						)
					snake_case__	     =model(**self.dummy_input						).sample

					assert image is not None, "Make sure output is not None"






			@unittest.skipIf(torch_device != 'cuda'					,   'This test is supposed to run on GPU'						)
			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__     ,	snake_case__	     =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'					,   output_loading_info=_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					snake_case__	     =model(**self.dummy_input						).sample

					assert image is not None, "Make sure output is not None"






			@unittest.skipIf(torch_device != 'cuda'					,   'This test is supposed to run on GPU'						)
			def 						_lowercase  (     self						)							->							Optional[Any]:
					# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
					snake_case__     ,	snake_case__	     =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'					,   output_loading_info=_UpperCAmelCase						)
					model_accelerate.to(_UpperCAmelCase						)
					model_accelerate.eval()

					snake_case__	     =torch.randn(
					    1					,   model_accelerate.config.in_channels					,   model_accelerate.config.sample_size					,   model_accelerate.config.sample_size					,   generator=torch.manual_seed(0						)					,   )
					snake_case__	     =noise.to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor([10] * noise.shape[0]						).to(_UpperCAmelCase						)

					snake_case__	     =model_accelerate(_UpperCAmelCase					,   _UpperCAmelCase						)['sample']

					# two models don't need to stay in the device at the same time
					del model_accelerate
					torch.cuda.empty_cache()
					gc.collect()

					snake_case__     ,	snake_case__	     =UNetaDModel.from_pretrained(
					    'fusing/unet-ldm-dummy-update'					,   output_loading_info=_UpperCAmelCase					,   low_cpu_mem_usage=_UpperCAmelCase						)
					model_normal_load.to(_UpperCAmelCase						)
					model_normal_load.eval()
					snake_case__	     =model_normal_load(_UpperCAmelCase					,   _UpperCAmelCase						)['sample']

					assert torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   rtol=1E-3						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'						)
					model.eval()
					model.to(_UpperCAmelCase						)

					snake_case__	     =torch.randn(
					    1					,   model.config.in_channels					,   model.config.sample_size					,   model.config.sample_size					,   generator=torch.manual_seed(0						)					,   )
					snake_case__	     =noise.to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor([10] * noise.shape[0]						).to(_UpperCAmelCase						)

					with torch.no_grad():
							snake_case__	     =model(_UpperCAmelCase					,   _UpperCAmelCase						).sample

					snake_case__	     =output[0, -1, -3:, -3:].flatten().cpu()
					# fmt: off
					snake_case__	     =torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800]						)
					# fmt: on

					self.assertTrue(torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   rtol=1E-3						)						)





class 							a__( snake_case__   ,      snake_case__   ,      unittest.TestCase     ):
			a_	: List[str]									=      UNetaDModel
			a_	: Optional[int]									=      '''sample'''






			@property
			def 						_lowercase  (     self					,   _UpperCAmelCase=(32, 32)						)							->							Tuple:
					snake_case__	     =4
					snake_case__	     =3

					snake_case__	     =floats_tensor((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor(batch_size * [10]						).to(dtype=torch.intaa					,   device=_UpperCAmelCase						)

					return {"sample": noise, "timestep": time_step}






			@property
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					return (3, 32, 32)






			@property
			def 						_lowercase  (     self						)							->							Optional[Any]:
					return (3, 32, 32)






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     ={
					    'block_out_channels': [32, 64, 64, 64],
					    'in_channels': 3,
					    'layers_per_block': 1,
					    'out_channels': 3,
					    'time_embedding_type': 'fourier',
					    'norm_eps': 1E-6,
					    'mid_block_scale_factor': math.sqrt(2.0						),
					    'norm_num_groups': None,
					    'down_block_types': [
					        'SkipDownBlock2D',
					        'AttnSkipDownBlock2D',
					        'SkipDownBlock2D',
					        'SkipDownBlock2D',
					    ],
					    'up_block_types': [
					        'SkipUpBlock2D',
					        'SkipUpBlock2D',
					        'AttnSkipUpBlock2D',
					        'SkipUpBlock2D',
					    ],
					}
					snake_case__	     =self.dummy_input
					return init_dict, inputs_dict






			@slow
			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__     ,	snake_case__	     =UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'					,   output_loading_info=_UpperCAmelCase						)
					self.assertIsNotNone(_UpperCAmelCase						)
					self.assertEqual(len(loading_info['missing_keys']						)					,   0						)

					model.to(_UpperCAmelCase						)
					snake_case__	     =self.dummy_input
					snake_case__	     =floats_tensor((4, 3) + (256, 256)						).to(_UpperCAmelCase						)
					snake_case__	     =noise
					snake_case__	     =model(**_UpperCAmelCase						)

					assert image is not None, "Make sure output is not None"






			@slow
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     =UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'						)
					model.to(_UpperCAmelCase						)

					snake_case__	     =4
					snake_case__	     =3
					snake_case__	     =(256, 256)

					snake_case__	     =torch.ones((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor(batch_size * [1E-4]						).to(_UpperCAmelCase						)

					with torch.no_grad():
							snake_case__	     =model(_UpperCAmelCase					,   _UpperCAmelCase						).sample

					snake_case__	     =output[0, -3:, -3:, -1].flatten().cpu()
					# fmt: off
					snake_case__	     =torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608]						)
					# fmt: on

					self.assertTrue(torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   rtol=1E-2						)						)






			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__	     =UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'						)
					model.to(_UpperCAmelCase						)

					snake_case__	     =4
					snake_case__	     =3
					snake_case__	     =(32, 32)

					snake_case__	     =torch.ones((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor(batch_size * [1E-4]						).to(_UpperCAmelCase						)

					with torch.no_grad():
							snake_case__	     =model(_UpperCAmelCase					,   _UpperCAmelCase						).sample

					snake_case__	     =output[0, -3:, -3:, -1].flatten().cpu()
					# fmt: off
					snake_case__	     =torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256]						)
					# fmt: on

					self.assertTrue(torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   rtol=1E-2						)						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					# not required for this model
					pass




